Updating blog with proper figures

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+banner.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,85 @@
 ---
 title: TemporalBenchEnv Blog
-emoji: 💻
-colorFrom: gray
-colorTo: red
+emoji: 🥁
+colorFrom: indigo
+colorTo: yellow
 sdk: static
 pinned: false
-license: apache-2.0
-short_description: Blog for TemporalBench for OpenEnv Comp in AgentX
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# TemporalBenchEnv Blog
+
+Static Hugging Face Space hosting the blog write-up for
+[`openenv-ts/TemporalBenchEnv`](../TemporalBenchEnv) — the OpenEnv-native
+multi-step MCQ environment that extends our Melady TS Green Agent
+submission into a GRPO training target.
+
+## Files
+
+| File | Purpose |
+| --- | --- |
+| `index.html` | Main blog page. Self-contained; pulls Inter + JetBrains Mono from Google Fonts, Mermaid from jsDelivr, MathJax from jsDelivr. |
+| `banner.png` | Hero image (3-panel: Question → Answer → Score). Generated by `build_banner.py`. |
+| `build_banner.py` | Matplotlib script that renders `banner.png`. Port of [`openenv-finance/impl-context/build_blog_images.py`](../../openenv-finance/impl-context/build_blog_images.py), palette-matched to the blog CSS variables. |
+| `README.md` | This file (also doubles as HF Space frontmatter). |
+
+## External links to fill in
+
+All external URLs in `index.html` that are not yet final are marked with
+`<TODO: ...>` / `<INSERT: ...>` placeholders. Search for `<TODO` to find
+them all. The current inventory:
+
+- GitHub repo URL for TemporalBenchEnv (hero badges, buttons, footer, quick start).
+- HF Space URL for TemporalBenchEnv (hero badges, buttons, topnav, footer, quick start).
+- Sibling `TemporalBenchPT` training-repo URL (architecture section, future work, quick start).
+- TS-Benchmark arXiv link (prior work, foundations).
+- TimeMaster / COUNTS / SenTSR-Bench arXiv links.
+- Melady TS Green Agent public repo / demo URL (green-agent section, foundations, footer).
+- Post-training numbers for the "Trained HF policy" row (results section).
+- LotteryElicitationEnv / ReasoningEconomicsEnv public URLs (prior work, engineering).
+
+## Regenerating `banner.png`
+
+Requires `matplotlib` and `numpy`:
+
+```bash
+pip install matplotlib numpy
+python build_banner.py                 # writes ./banner.png
+python build_banner.py -o /tmp/b.png   # custom path
+python build_banner.py --dpi 300       # higher DPI for print
+```
+
+The script uses the same theme constants (`BG`, `SURFACE`, `ACCENT`, `ACCENT2`,
+`GREEN`, `RED`, `ORANGE`) as `index.html`'s `:root` CSS variables so the
+banner stays visually consistent with the page.
+
+## Local preview
+
+```bash
+cd openenv-ts/TemporalBenchEnv_Blog
+python -m http.server 8000
+# open http://localhost:8000/index.html
+```
+
+Mermaid and MathJax render client-side over CDN, exactly as they do on the
+static HF Space, so local preview matches production.
+
+## Deploying as a Hugging Face static Space
+
+1. Create a new static Space (SDK: `static`) under the target namespace.
+2. Copy `index.html`, `banner.png`, and `README.md` to the Space repo.
+3. Commit and push — the Space serves `index.html` directly.
+
+The YAML frontmatter at the top of this README is the HF Space manifest;
+rename `sdk: static` / `title` to match the target Space if needed.
+
+## Style / structural source
+
+The blog structure, CSS palette, and component library (topnav, hero, TOC,
+episode trace, tables, mermaid-wrap, math-display, callout, notice panel) are
+ported from the sibling
+[`openenv-finance/LotteryElicitationEnv_Blog/index.html`](../../openenv-finance/LotteryElicitationEnv_Blog/index.html)
+per the shared
+[blog-creation-guidelines.md](../../openenv-finance/impl-context/blog-creation-guidelines.md).
+Keeping the structural drift between the two blogs near zero makes the
+Melady OpenEnv submissions read as a coherent series.

index.html

@@ -1,19 +1,816 @@
-<!doctype html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>TemporalBenchEnv: An OpenEnv Benchmark for Post-training LLMs on Multi-step Time-Series Reasoning</title>
+<meta name="description" content="TemporalBenchEnv: an OpenEnv-native multi-step MCQ environment for post-training LLMs on time-series reasoning, extending our Melady TS Green Agent submission with a verifiable-reward GRPO training path.">
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&family=JetBrains+Mono:wght@400;600&display=swap" rel="stylesheet">
+<!-- Mermaid for inline diagrams -->
+<script type="module">
+  import mermaid from 'https://cdn.jsdelivr.net/npm/mermaid@11/dist/mermaid.esm.min.mjs';
+  mermaid.initialize({
+    startOnLoad: true,
+    theme: 'dark',
+    themeVariables: {
+      primaryColor: '#6366f1',
+      primaryTextColor: '#e2e8f0',
+      primaryBorderColor: '#818cf8',
+      lineColor: '#818cf8',
+      secondaryColor: '#1e293b',
+      tertiaryColor: '#172033',
+      background: '#0f172a',
+      mainBkg: '#1e293b',
+      nodeBorder: '#818cf8',
+      clusterBkg: '#172033',
+      clusterBorder: '#334155',
+      titleColor: '#e2e8f0',
+      edgeLabelBackground: '#1e293b',
+      nodeTextColor: '#e2e8f0'
+    },
+    flowchart: { curve: 'basis', htmlLabels: true },
+    fontFamily: 'Inter, sans-serif'
+  });
+</script>
+<style>
+  :root {
+    --bg: #0f172a; --surface: #1e293b; --surface-2: #172033; --border: #334155;
+    --text: #e2e8f0; --muted: #94a3b8; --accent: #6366f1;
+    --accent2: #818cf8; --green: #22c55e; --red: #ef4444;
+    --orange: #f59e0b; --radius: 12px;
+  }
+  * { margin: 0; padding: 0; box-sizing: border-box; }
+  html { scroll-behavior: smooth; }
+  body { font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
+         background: var(--bg); color: var(--text); line-height: 1.7;
+         -webkit-font-smoothing: antialiased; }
+  .container { max-width: 820px; margin: 0 auto; padding: 2rem 1.5rem 4rem; }
+  /* Top nav */
+  .topnav { position: sticky; top: 0; z-index: 10; background: rgba(15,23,42,.85);
+            backdrop-filter: blur(10px); border-bottom: 1px solid var(--border);
+            padding: .9rem 1.5rem; display: flex; justify-content: space-between;
+            align-items: center; font-size: .88rem; }
+  .topnav .brand { font-weight: 700; color: var(--text); text-decoration: none;
+                   display: flex; align-items: center; gap: .5rem; }
+  .topnav .brand .dot { width: 8px; height: 8px; border-radius: 50%;
+                        background: var(--green); box-shadow: 0 0 8px rgba(34,197,94,.6); }
+  .topnav .links { display: flex; gap: 1.25rem; }
+  .topnav .links a { color: var(--muted); text-decoration: none; transition: color .15s; }
+  .topnav .links a:hover { color: var(--accent2); }
+  /* Hero */
+  .hero { text-align: center; padding: 4rem 0 2.5rem; }
+  .hero-badge { display: inline-block; background: rgba(99,102,241,.15); color: var(--accent2);
+                padding: .4rem 1.1rem; border-radius: 20px; font-size: .78rem; font-weight: 600;
+                letter-spacing: .08em; margin-bottom: 1.25rem;
+                border: 1px solid rgba(99,102,241,.3); text-transform: uppercase; }
+  .hero h1 { font-size: clamp(2rem, 4.2vw, 3.2rem); font-weight: 800; letter-spacing: -.025em;
+             line-height: 1.15;
+             background: linear-gradient(135deg, #e2e8f0 25%, #6366f1 100%);
+             -webkit-background-clip: text; -webkit-text-fill-color: transparent;
+             background-clip: text; }
+  .hero .subtitle { color: var(--muted); font-size: 1.15rem; max-width: 640px;
+                    margin: 1rem auto 0; }
+  .hero .byline { color: var(--muted); font-size: .85rem; margin-top: 1.5rem;
+                  font-style: italic; }
+  .banner { width: 100%; border-radius: var(--radius); margin: 2rem 0 3rem;
+            border: 1px solid var(--border); }
+  /* Badges row */
+  .badges { display: flex; justify-content: center; gap: .6rem; flex-wrap: wrap;
+            margin: 1.5rem 0; }
+  .badges img { height: 22px; }
+  /* Button group */
+  .btn-group { display: flex; gap: .75rem; justify-content: center; margin: 2rem 0;
+               flex-wrap: wrap; }
+  .btn { display: inline-flex; align-items: center; gap: .45rem; padding: .6rem 1.35rem;
+         background: var(--accent); color: white; border-radius: 8px; font-size: .88rem;
+         font-weight: 600; text-decoration: none; transition: all .2s; }
+  .btn:hover { background: var(--accent2); transform: translateY(-1px); }
+  .btn-outline { background: transparent; border: 1px solid var(--border); color: var(--text); }
+  .btn-outline:hover { border-color: var(--accent); color: var(--accent2);
+                       background: rgba(99,102,241,.08); }
+  /* TOC */
+  .toc { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius);
+         padding: 1.25rem 1.5rem; margin: 0 0 2.5rem; }
+  .toc h3 { font-size: .82rem; font-weight: 700; letter-spacing: .08em; text-transform: uppercase;
+            color: var(--accent2); margin-bottom: .85rem; }
+  .toc ol { list-style: none; counter-reset: toc; display: flex; flex-wrap: wrap; gap: .35rem .8rem;
+            margin: 0; padding: 0; }
+  .toc ol li { counter-increment: toc; font-size: .88rem; }
+  .toc ol li::before { content: counter(toc) "."; color: var(--accent); font-weight: 700;
+                       font-size: .8rem; margin-right: .3rem; }
+  .toc ol li a { color: var(--muted); text-decoration: none; transition: color .15s; }
+  .toc ol li a:hover { color: var(--accent2); }
+  /* Sections */
+  section { margin: 3.5rem 0; }
+  section h2 { font-size: 1.55rem; font-weight: 800; letter-spacing: -.01em;
+               margin-bottom: 1rem; color: var(--text);
+               border-left: 3px solid var(--accent); padding-left: .9rem; }
+  section h3 { font-size: 1.1rem; font-weight: 700; margin: 2rem 0 .75rem;
+               color: var(--accent2); }
+  section p { color: #cbd5e1; margin-bottom: 1rem; font-size: 1.02rem; }
+  section p strong { color: var(--text); }
+  section ul, section ol { color: #cbd5e1; margin: 1rem 0 1rem 1.5rem; }
+  section ul li, section ol li { margin-bottom: .5rem; font-size: 1rem; }
+  section ul li strong, section ol li strong { color: var(--text); }
+  /* Pull-quote */
+  blockquote { border-left: 3px solid var(--accent2);
+               background: rgba(99,102,241,.06); padding: 1.1rem 1.25rem;
+               margin: 1.5rem 0; border-radius: 0 8px 8px 0;
+               color: #e2e8f0; font-size: 1.02rem; }
+  /* Tables */
+  .table-wrap { margin: 1.5rem 0; overflow-x: auto;
+                background: var(--surface); border: 1px solid var(--border);
+                border-radius: var(--radius); }
+  table { width: 100%; border-collapse: collapse; font-size: .92rem; }
+  th { background: rgba(99,102,241,.1); color: var(--accent2);
+       font-size: .72rem; font-weight: 700; letter-spacing: .06em;
+       text-transform: uppercase; padding: .85rem 1rem; text-align: left; }
+  td { padding: .7rem 1rem; border-top: 1px solid var(--border); color: #cbd5e1; }
+  td.num { text-align: right; font-variant-numeric: tabular-nums;
+           font-family: 'JetBrains Mono', monospace; font-size: .88rem; }
+  tr:hover td { background: rgba(99,102,241,.04); }
+  td strong, th strong { color: var(--text); }
+  .task-id { font-family: 'JetBrains Mono', monospace; font-weight: 700;
+             color: var(--accent2); font-size: .85rem; }
+  tr.avg-row td { background: rgba(99,102,241,.08); font-weight: 700;
+                  color: var(--text); }
+  tr.novel td:first-child { color: #fca5a5; }
+  /* Code */
+  pre { background: #0b1120; border: 1px solid var(--border);
+        border-radius: var(--radius); padding: 1.1rem 1.25rem; overflow-x: auto;
+        margin: 1.25rem 0; font-family: 'JetBrains Mono', monospace;
+        font-size: .85rem; line-height: 1.6; color: #d1d5db; }
+  pre .c { color: #64748b; }
+  code { font-family: 'JetBrains Mono', monospace; font-size: .88em;
+         background: rgba(99,102,241,.12); color: var(--accent2);
+         padding: .1em .35em; border-radius: 4px; }
+  pre code { background: none; color: inherit; padding: 0; font-size: 1em; }
+  /* Figure */
+  figure { margin: 2rem 0; }
+  figure img { width: 100%; border-radius: var(--radius);
+               border: 1px solid var(--border); }
+  figcaption { text-align: center; color: var(--muted); font-size: .85rem;
+               margin-top: .75rem; }
+  /* Mermaid diagram wrapper */
+  .mermaid-wrap { margin: 2rem 0; background: var(--surface); border: 1px solid var(--border);
+                  border-radius: var(--radius); padding: 1.5rem 1rem; overflow-x: auto; }
+  .mermaid-wrap .mermaid { display: flex; justify-content: center; }
+  .mermaid-caption { text-align: center; color: var(--muted); font-size: .85rem;
+                     margin-top: .75rem; }
+  /* Episode trace */
+  .episode-trace { background: var(--surface); border: 1px solid var(--border);
+                   border-radius: var(--radius); padding: 1.25rem 1.5rem; margin: 1.5rem 0;
+                   position: relative; }
+  .episode-trace::before { content: ''; position: absolute; left: 1.5rem; top: 2.5rem;
+                           bottom: 1.25rem; width: 2px; background: var(--border); }
+  .trace-step { position: relative; padding-left: 2rem; margin-bottom: 1.25rem; }
+  .trace-step:last-child { margin-bottom: 0; }
+  .trace-step .step-marker { position: absolute; left: -.45rem; top: .2rem; width: 12px;
+                             height: 12px; border-radius: 50%; border: 2px solid var(--accent);
+                             background: var(--bg); z-index: 1; }
+  .trace-step .step-marker.terminal { background: var(--red); border-color: var(--red); }
+  .trace-step .step-marker.good { background: var(--green); border-color: var(--green); }
+  .trace-step .step-label { font-family: 'JetBrains Mono', monospace; font-size: .78rem;
+                            color: var(--accent2); font-weight: 700; margin-bottom: .25rem; }
+  .trace-step .step-content { font-size: .9rem; color: #cbd5e1; }
+  .trace-step .step-content code { font-size: .82em; }
+  .trace-verdict { margin-top: 1rem; padding: .75rem 1rem; border-radius: 8px;
+                   font-size: .9rem; font-weight: 600; }
+  .trace-verdict.bad { background: rgba(239,68,68,.1); border: 1px solid rgba(239,68,68,.3);
+                       color: #fca5a5; }
+  .trace-verdict.good { background: rgba(34,197,94,.1); border: 1px solid rgba(34,197,94,.3);
+                        color: #86efac; }
+  /* Callout for the closing question */
+  .callout { text-align: center; padding: 2rem 1.5rem; margin: 3rem 0;
+             background: linear-gradient(135deg, rgba(99,102,241,.08), rgba(129,140,248,.04));
+             border: 1px solid rgba(99,102,241,.25); border-radius: var(--radius); }
+  .callout .q { font-size: 1.25rem; font-weight: 700; color: var(--text);
+                font-style: italic; margin-bottom: .5rem; }
+  .callout .sub { color: var(--muted); font-size: .95rem; }
+  /* Notice panel (for "empirical results pending" etc.) */
+  .notice { padding: 1.1rem 1.25rem; margin: 1.5rem 0;
+            background: rgba(245,158,11,.08); border: 1px solid rgba(245,158,11,.3);
+            border-radius: var(--radius); color: #fde68a; font-size: .95rem; }
+  .notice strong { color: #fbbf24; }
+  /* Footer */
+  .footer { text-align: center; padding: 3rem 0 1rem; color: var(--muted);
+            font-size: .85rem; border-top: 1px solid var(--border); margin-top: 3rem; }
+  .footer a { color: var(--accent2); text-decoration: none; margin: 0 .5rem; }
+  .footer a:hover { text-decoration: underline; }
+  @media (max-width: 640px) {
+    .container { padding: 1rem 1rem 3rem; }
+    .hero { padding: 2.5rem 0 1.5rem; }
+    .topnav .links { display: none; }
+    section h2 { font-size: 1.3rem; }
+    table { font-size: .82rem; }
+    th, td { padding: .55rem .6rem; }
+    .toc ol { flex-direction: column; }
+    .episode-trace { padding: 1rem; }
+    .episode-trace::before { left: 1rem; }
+  }
+  /* MathJax display blocks */
+  .math-display {
+    margin: 1.25rem 0;
+    padding: 1rem 1.25rem 1.15rem;
+    overflow-x: auto;
+    background: var(--surface);
+    border: 1px solid var(--border);
+    border-radius: var(--radius);
+    text-align: center;
+  }
+  .math-display mjx-container[jax="CHTML"][display="true"] { margin: 0.65em 0 !important; }
+  .math-display mjx-container { color: #e2e8f0 !important; }
+  .math-note { font-size: .9rem; color: var(--muted); margin-top: .35rem; margin-bottom: 0; }
+</style>
+<!-- MathJax 3: client-side TeX like static HF Spaces (sdk: static) serve plain HTML. -->
+<script>
+window.MathJax = {
+  tex: {
+    inlineMath: [['\\(', '\\)']],
+    displayMath: [['\\[', '\\]']]
+  },
+  options: {
+    renderActions: {
+      addMenu: [0, '', '']
+    }
+  }
+};
+</script>
+<script defer src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js" id="MathJax-script"></script>
+</head>
+<body>
+
+<nav class="topnav">
+  <a href="#top" class="brand"><span class="dot"></span> TemporalBenchEnv Blog</a>
+  <div class="links">
+    <a href="#why">Why</a>
+    <a href="#green-agent">Green Agent</a>
+    <a href="#design">Design</a>
+    <a href="#scoring">Scoring</a>
+    <a href="#traces">Traces</a>
+    <a href="#results">Results</a>
+    <a href="#engineering">Engineering</a>
+    <a href="https://huggingface.co/spaces/yashu2000/TemporalBenchEnv" target="_blank">Live Space &#x2197;</a>
+  </div>
+</nav>
+
+<div class="container" id="top">
+
+  <div class="hero">
+    <div class="hero-badge">OpenEnv &middot; Extension of Melady TS Green Agent</div>
+    <h1>TemporalBenchEnv</h1>
+    <p class="subtitle">An OpenEnv-native multi-step MCQ environment for post-training LLMs on time-series reasoning &mdash; built on the four datasets from our Melady TS Green Agent submission.</p>
+    <div class="badges">
+      <a href="https://github.com/sharma-yash01/TemporalBenchEnv" target="_blank"><img src="https://img.shields.io/badge/GitHub-Repository-181717?logo=github" alt="GitHub"/></a>
+      <a href="https://huggingface.co/spaces/yashu2000/TemporalBenchEnv" target="_blank"><img src="https://img.shields.io/badge/HF%20Space-Live%20Demo-FFD21E?logo=huggingface&logoColor=black" alt="HF Space"/></a>
+      <img src="https://img.shields.io/badge/OpenEnv-Native-4B8BBE" alt="OpenEnv"/>
+      <img src="https://img.shields.io/badge/Datasets-4-brightgreen" alt="4 Datasets"/>
+      <img src="https://img.shields.io/badge/Episodes-9%20MCQ-blueviolet" alt="9-step MCQ"/>
+      <img src="https://img.shields.io/badge/Training-GRPO%20(planned)-orange" alt="GRPO (planned)"/>
+    </div>
+    <div class="byline">Melady / AgentX OpenEnv Track &nbsp;|&nbsp; Muyan Weng (USC) &middot; Defu Cao (USC) &middot; Yashaswi Sharma (USC) &middot; Yan Liu (USC)</div>
+  </div>
+
+  <img src="banner.png" alt="TemporalBenchEnv: question &rarr; answer &rarr; score across four time-series datasets" class="banner"/>
+
+  <div class="btn-group">
+    <a class="btn" href="https://huggingface.co/spaces/yashu2000/TemporalBenchEnv" target="_blank">Live Environment Space &rarr;</a>
+    <a class="btn btn-outline" href="https://github.com/sharma-yash01/TemporalBenchEnv" target="_blank">GitHub Repo</a>
+  </div>
+
+  <!-- Table of Contents -->
+  <nav class="toc" id="toc">
+    <h3>Contents</h3>
+    <ol>
+      <li><a href="#why">Multi-step TS Reasoning as a Verifiable Env</a></li>
+      <li><a href="#green-agent">Extension of Our Green Agent Submission</a></li>
+      <li><a href="#purple-harnesses">Purple Agent Harnesses (Eval Targets)</a></li>
+      <li><a href="#matters">Why This Benchmark Matters</a></li>
+      <li><a href="#prior-work">Prior Work &amp; Novelty</a></li>
+      <li><a href="#design">What TemporalBenchEnv Is</a></li>
+      <li><a href="#env-design">Environment Design</a></li>
+      <li><a href="#openenv">Why OpenEnv</a></li>
+      <li><a href="#scoring">Scoring: Per-step + Episode Bonus</a></li>
+      <li><a href="#architecture">Architecture &amp; Training Pipeline</a></li>
+      <li><a href="#traces">Episode Trace (Ideal, Illustrative)</a></li>
+      <li><a href="#results">Results: Baselines &amp; Projected Targets</a></li>
+      <li><a href="#engineering">Engineering Lessons (Inherited)</a></li>
+      <li><a href="#foundations">Foundations &amp; Citations</a></li>
+      <li><a href="#quickstart">Quick Start</a></li>
+      <li><a href="#future">Future Work</a></li>
+    </ol>
+  </nav>
+
+  <!-- 1. WHY -->
+  <section id="why">
+    <h2>Multi-step TS reasoning as a verifiable environment</h2>
+    <p>Most time-series LLM benchmarks grade a single prompt at a time. <strong>TemporalBenchEnv</strong> grades an <strong>episode</strong>: nine multiple-choice questions drawn from four time-series datasets, answered one per step, with a terminal bonus that rewards both accuracy and <strong>cross-domain coverage</strong>.</p>
+    <!-- TODO: Replace OpenReview link with arXiv link when available -->
+    <p>Every reward signal here is <strong>ground-truth arithmetic</strong>, not a judge. Labels are produced by the <a href="https://openreview.net/forum?id=rp5qCsxXca&noteId=jYZcZ4GI0y" target="_blank" style="color:var(--accent2)">TS-Benchmark</a> construction pipeline (trend / volatility / seasonality / outlier thresholds; S1&ndash;S5 family rules), so the environment can score an answer with a normalized string match against the stored ground truth. There is no LLM judge in the loop.</p>
+    <p>The falsifiable hypothesis this environment is built to test: <em>whether a GRPO-trained LLM, post-trained on sequential episodes sampled from our Green Agent&rsquo;s own benchmark, outperforms strong zero-shot baselines on per-domain MCQ accuracy while hitting the cross-domain coverage bonus.</em> Empirical adjudication is contingent on the training runs described under <a href="#architecture" style="color:var(--accent2)">Architecture &amp; training pipeline</a>.</p>
+  </section>
+
+  <!-- 2. EXTENSION OF GREEN AGENT -->
+  <section id="green-agent">
+    <h2>Extension of our Melady TS Green Agent submission</h2>
+    <p>TemporalBenchEnv is a <strong>direct extension</strong> of our AgentBeats <a href="https://agentbeats.dev/sharma-yash01/melady-ts-green-agent" target="_blank" style="color:var(--accent2)"><strong>Melady TS Green Agent</strong></a> submission. The Green Agent is an A2A-protocol evaluator that grades purple agents on 764 TS-Benchmark tasks (see the <a href="https://github.com/sharma-yash01/Melady_Agent-TS-Green" target="_blank" style="color:var(--accent2)">Green Agent GitHub repository</a>). TemporalBenchEnv takes the <em>same datasets and task taxonomy</em> and re-exposes them as a sequential <strong>OpenEnv</strong> environment consumable by TRL&rsquo;s <code>rollout_func</code> &mdash; turning the benchmark into a <strong>training target</strong>.</p>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Artifact</th><th>Melady TS Green Agent</th><th>TemporalBenchEnv (this submission)</th></tr></thead>
+        <tbody>
+          <tr><td><strong>Role</strong></td><td>A2A evaluator of purple agents</td><td>OpenEnv RL environment for post-training LLMs</td></tr>
+          <tr><td><strong>Datasets</strong></td><td>PSML, freshretailnet, MIMIC, causal_chambers</td><td>Same four datasets</td></tr>
+          <tr><td><strong>Tasks</strong></td><td>T1/T3 (accuracy) + T2/T4 (regression + MCQ), 764 total</td><td>MCQ subset: T1U, T3, T2_MCQ &mdash; 2,775 <code>TSQuestion</code> records</td></tr>
+          <tr><td><strong>Per-domain bank sizes</strong></td><td>Packaged in the Docker image</td><td>PSML 750 &middot; freshretailnet 616 &middot; MIMIC 709 &middot; causal_chambers 700</td></tr>
+          <tr><td><strong>Protocol</strong></td><td>A2A messaging, one-shot prompts</td><td>WebSocket OpenEnv contract, sequential 9-step MDP</td></tr>
+          <tr><td><strong>Reward</strong></td><td>MSE / MAE / RMSE / MASE / accuracy (eval metrics)</td><td>Per-step correctness + terminal episode bonus w/ coverage multiplier</td></tr>
+          <tr><td><strong>Consumer</strong></td><td>AgentBeats leaderboard</td><td>TRL 1.0 <code>rollout_func</code>, vLLM colocate / server, GRPO</td></tr>
+        </tbody>
+      </table>
+    </div>
+    <p>The ETL from the Green Agent&rsquo;s labeled JSONL into the per-domain <code>TSQuestion</code> banks the environment consumes lives in <code>TS-benchmark/scripts/build_temporal_bench_openenv_banks.py</code>; banks ship at <code>openenv-ts/TemporalBenchEnv/data/banks/</code> and are loaded via the <code>TEMPORALBENCH_QUESTION_BANK_DIR</code> environment variable.</p>
+    <blockquote>The Green Agent answered: <em>which purple agent is best at TS reasoning right now?</em> &nbsp;TemporalBenchEnv answers: <em>can we post-train an LLM, on that exact benchmark, to be the next best purple agent?</em></blockquote>
+  </section>
+
+  <!-- 2b. PURPLE AGENT HARNESSES -->
+  <section id="purple-harnesses">
+    <h2>Purple agent harnesses: evaluating mainstream TS-capable agent stacks</h2>
+    <p>The Green Agent scores <em>purple agents</em> over the A2A protocol &mdash; any stack that speaks A2A is a valid participant. To make the benchmark diagnostic for modern agentic time-series practice, we target four of the most popular open-source agent harnesses as purple agents on AgentBeats: <strong>two are implemented and live</strong> (AgentScope, CAMEL) and <strong>two are planned</strong> (MetaGPT, TimeSeriesScientist). Each harness stays <em>unchanged internally</em>; a thin A2A adapter feeds the Green Agent&rsquo;s TS-Benchmark MCQs into the harness&rsquo;s own reasoning / tool-use loop and returns a final label. These four frameworks are the de-facto &ldquo;agentic harnesses&rdquo; TS practitioners actually wrap around LLMs today, so they anchor the eval to real downstream usage.</p>
+
+    <div class="notice">
+      <strong>The feedback loop we are instrumenting.</strong> For every harness we expose a swappable backbone LLM (e.g. Qwen2.5-7B-Instruct or GPT-4o-mini). The plan is: <strong>(1)</strong> score the base backbone inside each harness via the <a href="https://agentbeats.dev/sharma-yash01/melady-ts-green-agent" target="_blank" style="color:var(--accent2)">Melady TS Green Agent</a>, <strong>(2)</strong> <strong>post-train that same backbone with this OpenEnv</strong> &mdash; TemporalBenchEnv&rsquo;s randomized-domain / randomized-task 9-step MCQ episodes with per-step + terminal verifiable rewards, <strong>(3)</strong> <strong>re-score every harness</strong> with the post-trained backbone in place of the base, and <strong>(4)</strong> attribute any delta specifically to the RL post-training rather than to harness architecture. The panel makes the research question concrete: <em>does randomized-domain verifiable-reward post-training on TemporalBenchEnv actually transfer to agentic TS reasoning under mainstream orchestration frameworks?</em>
+    </div>
+
+    <h3>Implemented &mdash; live purple agents on AgentBeats</h3>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Harness</th><th>What it is</th><th>Why it matters for TS reasoning</th><th>AgentBeats listing</th></tr></thead>
+        <tbody>
+          <tr>
+            <td><strong><a href="https://github.com/agentscope-ai/agentscope" target="_blank" style="color:var(--accent2)">AgentScope</a></strong><br><span style="font-size:.85em;color:var(--muted)">Gao et al., <a href="https://arxiv.org/abs/2508.16279" target="_blank" style="color:var(--accent2)">&ldquo;AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications&rdquo; (arXiv:2508.16279, 2025)</a>; Gao et al., <a href="https://arxiv.org/abs/2402.14034" target="_blank" style="color:var(--accent2)">&ldquo;AgentScope: A Flexible yet Robust Multi-Agent Platform&rdquo; (arXiv:2402.14034, 2024)</a></span></td>
+            <td>Production-ready ReAct agent framework with built-in tools, memory, planning, MCP / A2A interop, and an agentic-RL tuner (Trinity-RFT). Apache-2.0.</td>
+            <td>The most common Python-native ReAct / tool-use stack; A2A support makes the Green Agent wiring mechanical. Serves as our canonical &ldquo;single-ReAct-loop&rdquo; purple baseline over TS MCQs.</td>
+            <td><a href="https://agentbeats.dev/sharma-yash01/melady-ts-purple-agentscope" target="_blank" style="color:var(--accent2)">Melady TS Purple (AgentScope)</a></td>
+          </tr>
+          <tr>
+            <td><strong><a href="https://github.com/camel-ai/camel" target="_blank" style="color:var(--accent2)">CAMEL</a></strong><br><span style="font-size:.85em;color:var(--muted)">Li et al., <a href="https://arxiv.org/abs/2303.17760" target="_blank" style="color:var(--accent2)">&ldquo;CAMEL: Communicative Agents for &lsquo;Mind&rsquo; Exploration of Large Language Model Society&rdquo; (NeurIPS 2023, arXiv:2303.17760)</a></span></td>
+            <td>Role-playing multi-agent framework with stateful memory, structured messages, societies / workforce pipelines, and a strong focus on <em>scaling laws of agents</em>.</td>
+            <td>Role-play + critic loop tests whether a trend / seasonality / outlier answer survives multi-turn discussion without drifting; complements AgentScope&rsquo;s single-ReAct shape with an inter-agent communication surface.</td>
+            <td><a href="https://agentbeats.dev/sharma-yash01/melady-ts-base-purple-agent" target="_blank" style="color:var(--accent2)">Melady TS Base Purple Agent</a></td>
+          </tr>
+        </tbody>
+      </table>
+    </div>
+
+    <h3>Planned &mdash; purple agents under development</h3>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Harness</th><th>What it is</th><th>Why it matters for TS reasoning</th><th>Status</th></tr></thead>
+        <tbody>
+          <tr>
+            <td><strong><a href="https://github.com/FoundationAgents/MetaGPT" target="_blank" style="color:var(--accent2)">MetaGPT</a></strong><br><span style="font-size:.85em;color:var(--muted)">Hong et al., <a href="https://arxiv.org/abs/2308.00352" target="_blank" style="color:var(--accent2)">&ldquo;MetaGPT: Meta Programming for a Multi-Agent Collaborative Framework&rdquo; (ICLR 2024, oral; arXiv:2308.00352)</a></span></td>
+            <td>SOP-driven multi-agent system &mdash; explicit role decomposition (product manager / architect / engineer / QA) orchestrated by a meta-programming layer. One of the most-starred multi-agent frameworks in the wild.</td>
+            <td>Gives us a decomposition-heavy purple: a &ldquo;data analyst&rdquo; + &ldquo;forecaster&rdquo; / &ldquo;reviewer&rdquo; role split answering the same MCQs, isolating whether explicit SOPs help TS reasoning vs. a flat ReAct loop.</td>
+            <td><strong>Planned.</strong> A2A wrapper not yet published on AgentBeats.</td>
+          </tr>
+          <tr>
+            <td><strong><a href="https://github.com/Y-Research-SBU/TimeSeriesScientist" target="_blank" style="color:var(--accent2)">TimeSeriesScientist (TSci)</a></strong><br><span style="font-size:.85em;color:var(--muted)">Zhao et al., <a href="https://arxiv.org/abs/2510.01538" target="_blank" style="color:var(--accent2)">&ldquo;TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis&rdquo; (arXiv:2510.01538, 2025)</a></span></td>
+            <td>Domain-specific LangGraph agent purpose-built for TS: <em>Curator &rarr; Planner &rarr; Forecaster &rarr; Reporter</em>, with statistical / ML / DL model selection and ensembling (ARIMA, Prophet, LSTM, XGBoost, Transformer, &hellip;).</td>
+            <td>The strongest TS-specialized agent we can find; a natural &ldquo;ceiling&rdquo; for agentic TS reasoning and a direct yardstick for whether an RL-post-trained generalist LLM inside a simpler harness closes the gap to a purpose-built TS pipeline.</td>
+            <td><strong>Planned.</strong> A2A adapter not yet published on AgentBeats.</td>
+          </tr>
+        </tbody>
+      </table>
+    </div>
+
+    <blockquote><strong>Green Agent + TemporalBenchEnv + harness panel = a closed evaluation loop.</strong> The Green Agent scores every harness on a fixed TS-Benchmark MCQ set; TemporalBenchEnv uses the <em>same datasets and task taxonomy</em> to post-train a backbone with randomized-domain / randomized-task episodes under verifiable rewards; we then drop the post-trained backbone back into AgentScope, CAMEL, MetaGPT, and TSci and re-score. A delta from the RL post-training should show up as a <em>uniform</em> lift across all four harnesses &mdash; not just in one framework, and not just in the zero-shot MCQ row of the baselines table.</blockquote>
+  </section>
+
+  <!-- 3. WHY IT MATTERS -->
+  <section id="matters">
+    <h2>Why this benchmark matters</h2>
+    <p>Time-series reasoning is one of the few areas where frontier LLMs still look visibly weak, but also one where the <strong>verifiable signal is clean</strong>: trend / volatility / seasonality / outlier labels are constructed from thresholded statistics of the series, so exact-match grading is unambiguous. That makes MCQ episodes a near-ideal RL target before touching noisier numeric forecasting rewards.</p>
+    <p>The design is <strong>transferable</strong>. Any benchmark that produces labeled MCQ records over a set of domains &mdash; medical diagnostics, power-grid anomaly tagging, retail demand regimes &mdash; fits the same 9-question cross-domain template. Datasets are the proxy; the capability is <strong>multi-step, multi-domain, verifiable TS reasoning</strong>.</p>
+    <p>Every reward component is <strong>ground-truth arithmetic</strong>. The environment samples questions from pre-built banks, scores answers via normalized string equality, and aggregates with a closed-form episode bonus. No LLM judge, no circular reward.</p>
+  </section>
+
+  <!-- 4. PRIOR WORK & NOVELTY -->
+  <section id="prior-work">
+    <h2>Prior work &amp; novelty</h2>
+    <p>Prior &ldquo;LLMs + time series&rdquo; work lands in one of three buckets. None occupies the cell we target:</p>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Prior work bucket</th><th>What it does</th><th>What it does not</th></tr></thead>
+        <tbody>
+          <tr><td><strong>Static TS benchmarks</strong><br><span style="font-size:.85em;color:var(--muted)">TS-Benchmark (ours, <a href="https://openreview.net/forum?id=rp5qCsxXca&noteId=jYZcZ4GI0y" target="_blank" style="color:var(--accent2)">OpenReview</a>); FreshRetailNet (<a href="https://arxiv.org/abs/2505.16319" target="_blank" style="color:var(--accent2)">arXiv:2505.16319</a>); PSML (<a href="https://www.nature.com/articles/s41597-022-01455-7" target="_blank" style="color:var(--accent2)">Nat. Sci. Data 2022</a>); MIMIC-IV (<a href="https://www.nature.com/articles/s41597-022-01899-x" target="_blank" style="color:var(--accent2)">Nat. Sci. Data 2022</a>); Causal Chambers (<a href="https://www.nature.com/articles/s42256-024-00964-x" target="_blank" style="color:var(--accent2)">Nat. MI 2024</a>)</span></td><td>Construct labeled MCQ / regression tasks over TS datasets, graded by fixed rules</td><td>No RL-native environment contract, no sequential episodes, no post-training loop</td></tr>
+          <tr><td><strong>TS-LLM composite RL rewards</strong><br><span style="font-size:.85em;color:var(--muted)">TimeMaster (<a href="https://arxiv.org/abs/2506.13705" target="_blank" style="color:var(--accent2)">arXiv:2506.13705</a>); COUNTS (<a href="https://arxiv.org/abs/2510.01116" target="_blank" style="color:var(--accent2)">arXiv:2510.01116</a>); SenTSR-Bench (<a href="https://arxiv.org/abs/2602.19455" target="_blank" style="color:var(--accent2)">arXiv:2602.19455</a>)</span></td><td>Composite reward shaping (MSE + DTW + direction + quantile + ArcTan) for TS multimodal LLMs</td><td>No OpenEnv contract, no sequential MCQ episodes, reward is forecasting-centric</td></tr>
+          <tr><td><strong>A2A evaluators</strong><br><span style="font-size:.85em;color:var(--muted)">Melady TS Green Agent (<a href="https://agentbeats.dev/sharma-yash01/melady-ts-green-agent" target="_blank" style="color:var(--accent2)">AgentBeats</a>); other AgentBeats green agents</span></td><td>Score deployed purple agents on the benchmark through the A2A protocol</td><td>Not a training environment; no per-step RL signal, no environment state</td></tr>
+          <tr class="novel"><td><strong>TemporalBenchEnv (ours)</strong></td><td>Sequential 9-step MCQ MDP over four TS datasets, structured Pydantic actions, terminal reward from ground-truth labels + coverage multiplier, OpenEnv + GRPO contract</td><td>Does not (yet) train numeric forecasting &mdash; T2/T4 reward is stubbed for future work</td></tr>
+        </tbody>
+      </table>
+    </div>
+    <blockquote>To our knowledge, no prior work exposes TS-Benchmark&rsquo;s multi-dataset MCQ suite as an <strong>OpenEnv-native sequential MDP with verifiable terminal rewards</strong> suitable for GRPO post-training. The action space, reward decomposition, and sibling-env architecture follow the <a href="https://huggingface.co/spaces/yashu2000/LotteryElicitationEnv" target="_blank" style="color:var(--accent2)">LotteryElicitationEnv</a> lineage; the domain and task taxonomy follow our own Melady TS Green Agent.</blockquote>
+  </section>
+
+  <!-- 5. WHAT IT IS -->
+  <section id="design">
+    <h2>What TemporalBenchEnv is</h2>
+    <blockquote>An OpenEnv-native sequential MDP in which an LLM agent answers nine MCQ questions &mdash; six from a primary domain and one each from the other three &mdash; earning per-step correctness and a terminal bonus that rewards cross-domain coverage.</blockquote>
+    <p>Each episode proceeds like this:</p>
+    <ul>
+      <li><code>reset()</code> samples nine <code>TSQuestion</code> records: <strong>6 from the primary domain</strong> (default PSML), with T3 families round-robined for diversity, and <strong>1 from each of the three non-primary domains</strong>. Final order is shuffled.</li>
+      <li>On turn <em>t</em>, the agent emits a <code>TemporalBenchAction</code> containing the MCQ label, plus optional <code>confidence</code> and <code>reasoning</code> fields.</li>
+      <li>The environment <strong>grades</strong> the answer via normalized string equality against <code>question.answer</code> (also accepting an option whose normalized text matches the ground truth), and returns the next question.</li>
+      <li>Per-step reward is <code>alpha * correctness</code>. Mid-episode bonuses are zero.</li>
+      <li>On the final step, the environment adds the <strong>terminal episode bonus</strong>: <code>lambda_ep * (total_correct / N) * coverage_multiplier</code>, where the multiplier is <code>1.0</code> if every one of the four domains contributed at least one correct answer, else <code>0.8</code>.</li>
+    </ul>
+    <p>The agent&rsquo;s interface is deliberately minimal: a single answer string per step, no tool-call protocol. Optional <code>confidence</code> and <code>reasoning</code> fields exist on the action for future reward shaping.</p>
+  </section>
+
+  <!-- 6. ENVIRONMENT DESIGN -->
+  <section id="env-design">
+    <h2>Environment design</h2>
+    <p>The core contract is three Pydantic types exchanged over the OpenEnv WebSocket (see <code>openenv-ts/TemporalBenchEnv/env/models.py</code>):</p>
+    <pre><code><span class="c"># Action (agent &rarr; env)</span>
+class TemporalBenchAction(Action):
+    answer: str                         <span class="c"># MCQ label matching an option</span>
+    confidence: Optional[float]         <span class="c"># in [0, 1], unused in reward for now</span>
+    reasoning: Optional[str]            <span class="c"># optional CoT, unused in reward for now</span>
+
+<span class="c"># Observation (env &rarr; agent)</span>
+class TemporalBenchObservation(Observation):
+    step_idx, steps_remaining, max_steps: int
+    question: str                       <span class="c"># current MCQ prompt</span>
+    options: list[str]                  <span class="c"># 2+ answer choices</span>
+    task_type: str                      <span class="c"># "T1U" | "T3" | "T2_MCQ"</span>
+    dataset: str                        <span class="c"># "PSML" | "freshretailnet" | "MIMIC" | "causal_chambers"</span>
+    history: list[dict]                 <span class="c"># [{question, answer, correct, dataset, ...}, ...]</span>
+    accuracy_so_far: float
+    done: bool; reward: Optional[float]; metadata: dict
+
+<span class="c"># State (serializable snapshot)</span>
+class TemporalBenchState(State):
+    episode_id: Optional[str]
+    step_count, total_correct, total_questions: int
+    current_accuracy: float
+    primary_domain: str
+    per_task_type_accuracy: dict[str, float]
+    total_reward: float</code></pre>
+
+    <p>Four datasets, three MCQ task types, and a three-stage curriculum shape the training distribution (see <code>env/config.py</code> and <code>env/episode_sampler.py</code>):</p>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Stage</th><th>Allowed task types</th><th>Purpose</th></tr></thead>
+        <tbody>
+          <tr><td><strong>Stage 1</strong></td><td><code>T1U</code> only (non-contextual understanding MCQ)</td><td>Shorten credit assignment; learn trend / volatility / seasonality / outliers first</td></tr>
+          <tr><td><strong>Stage 2</strong></td><td><code>T1U</code> + <code>T3</code> (contextual understanding, S1&ndash;S5 families)</td><td>Add context-conditioned reasoning; maintain verifiable labels</td></tr>
+          <tr><td><strong>Stage 3</strong></td><td><code>T1U</code> + <code>T3</code> + <code>T2_MCQ</code> (prediction-as-classification)</td><td>Full MCQ track; adds direction-of-change / volatility-change / seasonality-alignment</td></tr>
+        </tbody>
+      </table>
+    </div>
+    <p>Curriculum is honored both at <code>EnvConfig(curriculum_stage=...)</code> construction and at <code>env.reset(curriculum_stage=...)</code>, so a single server can serve multiple stages to different sessions concurrently.</p>
+  </section>
+
+  <!-- 7. WHY OPENENV -->
+  <section id="openenv">
+    <h2>Why OpenEnv</h2>
+    <p>OpenEnv gives us three things that matter for this submission: (1) a standard WebSocket environment contract consumable by TRL&rsquo;s <code>rollout_func</code>, (2) per-session state with <code>max_concurrent_envs=64</code> in our <code>create_app</code> factory &mdash; each WebSocket session gets a fresh <code>TemporalBenchEnvironment</code> via <code>_env_factory</code> so DDP ranks can hammer the same Space without cross-talk, and (3) a uniform deployment path. The same env code runs in-process for tests, as a Docker container for development (<code>server/Dockerfile</code>, with <code>TEMPORALBENCH_QUESTION_BANK_DIR=/app/env/data/banks</code>), and as a Hugging Face Space during training and evaluation.</p>
+    <p>No new abstractions were invented. Base types only: <code>EnvClient</code>, <code>Environment</code>, Pydantic <code>Action</code> / <code>Observation</code> / <code>State</code>. All extensions (history, per-domain coverage, per-task accuracy) ride on <code>metadata</code> or the serialized state. No new method signatures, no fork. The env ships with <code>openenv.yaml</code> and a <code>Dockerfile</code>, and passes <code>uv run openenv validate</code>.</p>
+    <p class="math-note"><strong>Hygiene note:</strong> OpenEnv&rsquo;s CLI validator does a naive substring check for <code>main()</code> in <code>server/app.py</code>. We match the reference pattern from <a href="https://huggingface.co/spaces/yashu2000/LotteryElicitationEnv" target="_blank" style="color:var(--accent2)">LotteryElicitationEnv</a> &mdash; an explicit <code>main()</code> call under <code>if __name__ == "__main__"</code> with CLI flags parsed via <code>parse_known_args</code>.</p>
+  </section>
+
+  <!-- 8. SCORING -->
+  <section id="scoring">
+    <h2>Scoring: per-step correctness + episode bonus</h2>
+    <p>Reward is decomposed into a per-step term and a terminal bonus (see <code>env/reward.py</code>).</p>
+    <div class="math-display" aria-label="Per-step correctness reward">
+\[
+r_t \;=\; \alpha \cdot \mathbf{1}\!\left[\hat a_t = a_t^\ast\right]
+\]
+    </div>
+    <p class="math-note">Here \(\hat a_t\) is the agent&rsquo;s submitted label and \(a_t^\ast\) is the stored ground truth, compared under normalized string equality (also accepting an option whose normalized text matches).</p>
+    <div class="math-display" aria-label="Terminal episode bonus with coverage multiplier">
+\[
+B \;=\; \lambda_{\mathrm{ep}} \,\cdot\, \frac{C}{N} \,\cdot\, m,
+\qquad
+m \;=\; \begin{cases} 1.0 & \text{all 4 domains have } \ge 1 \text{ correct} \\ 0.8 & \text{otherwise} \end{cases}
+\]
+\[
+R \;=\; \sum_{t=1}^{N} r_t \;+\; B
+\]
+    </div>
+    <p class="math-note">\(C\) is the total correct count in the episode, \(N = 9\) is the episode length, \(\alpha, \lambda_{\mathrm{ep}}\) are <code>alpha</code> and <code>lambda_ep</code> in <code>EnvConfig</code>.</p>
+    <p>Defaults live in <code>EnvConfig</code>:</p>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Component</th><th>Weight</th><th>What it rewards</th></tr></thead>
+        <tbody>
+          <tr><td><strong>Per-step correctness</strong></td><td class="num">&alpha; = 1.0</td><td>Normalized-string match against the MCQ ground truth</td></tr>
+          <tr><td><strong>Episode bonus weight</strong></td><td class="num">&lambda;<sub>ep</sub> = 0.5</td><td>Scales the terminal accuracy&times;coverage term</td></tr>
+          <tr><td><strong>Coverage multiplier</strong></td><td class="num">{0.8, 1.0}</td><td>1.0 iff every domain in <code>EnvConfig.all_domains</code> has &ge;1 correct answer this episode</td></tr>
+          <tr><td><strong>Forecasting reward</strong></td><td class="num">&mdash;</td><td>Stubbed (<code>compute_forecasting_reward</code> raises <code>NotImplementedError</code>); future work</td></tr>
+        </tbody>
+      </table>
+    </div>
+    <blockquote><strong>Why a coverage multiplier:</strong> per-step accuracy alone lets the agent ace the six primary questions while guessing on the other three domains, collapsing to a single-domain policy. The <code>0.8</code> penalty forces the policy to treat the three cross-domain questions as first-class signal &mdash; the very thing that distinguishes a TS-generalist from a PSML-only memorizer.</blockquote>
+  </section>
+
+  <!-- 9. ARCHITECTURE & TRAINING PIPELINE -->
+  <section id="architecture">
+    <h2>Architecture &amp; training pipeline</h2>
+    <p>Following the <a href="https://huggingface.co/spaces/yashu2000/LotteryElicitationEnv" target="_blank" style="color:var(--accent2)">LotteryElicitationEnv</a> / <a href="https://huggingface.co/spaces/yashu2000/reasoning-economic-env" target="_blank" style="color:var(--accent2)">ReasoningEconomicsEnv</a> lineage, <strong>TemporalBenchEnv</strong> (this page) ships the OpenEnv side: reset, step, rewards, and question banks over WebSocket. A separate trainer process &mdash; same env/trainer separation as <code>LotteryElicitationEnv</code> and <a href="https://github.com/sharma-yash01/LotteryElicitationPT" target="_blank" style="color:var(--accent2)"><code>LotteryElicitationPT</code></a> &mdash; would drive GRPO with TRL&rsquo;s <code>rollout_func</code> and vLLM against that socket, without in-process imports of env-side types.</p>
+
+    <div class="notice">
+      <strong>Training client status.</strong> The companion GRPO / TRL package that runs rollouts against this environment is under <strong>active internal testing</strong>. We will release it on <strong>GitHub</strong> once we secure <strong>additional compute</strong> so we can stress-test and validate models at a scale we consider reasonable. This blog documents the shipped <strong>TemporalBenchEnv</strong> only until that release.
+    </div>
+
+    <blockquote><strong>Purple agents on AgentBeats.</strong> The same TS-Benchmark task surface is also exercised by <strong>purple agents</strong> scored through the A2A green agent: live listings are <a href="https://agentbeats.dev/sharma-yash01/melady-ts-purple-agentscope" target="_blank" style="color:var(--accent2)">Melady TS Purple (AgentScope)</a> and <a href="https://agentbeats.dev/sharma-yash01/melady-ts-base-purple-agent" target="_blank" style="color:var(--accent2)">Melady TS Base Purple Agent</a>. Planned additions are documented in <a href="#purple-harnesses" style="color:var(--accent2)">&sect;&nbsp;Purple agent harnesses</a>.</blockquote>
+
+    <div class="mermaid-wrap">
+      <pre class="mermaid">
+flowchart LR
+    subgraph TRN ["Trainer (TRL + GRPO, WebSocket client)"]
+        GRPO["GRPOTrainer<br/>TRL 1.0"]
+        RF["rollout_func"]
+        VLLM["vLLM<br/>colocate/server"]
+        PARSE["action_parser<br/>MCQ label guardrails"]
+    end
+    subgraph ENV ["TemporalBenchEnv (OpenEnv)"]
+        WS["FastAPI<br/>WebSocket"]
+        SAMP["EpisodeSampler<br/>6+1+1+1 stratified"]
+        GRADE["grade_answer<br/>normalized match"]
+        REW["Reward<br/>per-step + bonus + coverage"]
+    end
+
+    GRPO --> RF
+    RF --> VLLM
+    VLLM -->|"generate"| PARSE
+    PARSE -->|"answer string"| WS
+    WS --> SAMP
+    SAMP -->|"next question"| WS
+    WS --> GRADE
+    GRADE --> REW
+    REW -->|"step + terminal reward"| WS
+    WS -->|"observation"| RF
+      </pre>
+      <p class="mermaid-caption">Figure 1. System architecture. The trainer never imports env-side types &mdash; everything crosses the WebSocket, exactly like our Lottery / Reasoning sibling envs.</p>
+    </div>
+
+    <p>Training uses <strong>GRPO</strong> (Group Relative Policy Optimization), which is a natural fit for per-step verifiable rewards with an additive terminal bonus. The training scaffolding is directly inherited from <a href="https://github.com/sharma-yash01/LotteryElicitationPT" target="_blank" style="color:var(--accent2)"><code>LotteryElicitationPT</code></a>: TRL 1.0&rsquo;s <code>rollout_func</code> contract, vLLM colocate/server, chat-template tokenization with <code>enable_thinking=False</code>, think-block stripping, null-safe MCQ-label parsing, and episode logging to <code>reward_logs.jsonl</code>.</p>
+  </section>
+
+  <!-- 10. EPISODE TRACE (IDEAL, ILLUSTRATIVE) -->
+  <section id="traces">
+    <h2>Episode trace (Ideal, Illustrative)</h2>
+    <p>Here is what a high-reward episode <em>would</em> look like &mdash; five representative steps out of nine, spanning all four domains. Primary domain is PSML; the trace shows the three cross-domain picks plus two primary turns, culminating in the terminal step where the coverage multiplier decides the shape of the bonus. <strong>Turns 2, 4, 6, 8 are elided</strong> (all PSML T1U, assumed correct for the 7 / 9 total) so the walkthrough stays focused on the cross-domain structure.</p>
+
+    <div class="notice">
+      <strong>Illustrative, not captured.</strong> This is a hand-constructed walkthrough intended to explain the per-step reward, the 6 + 3 domain split, and the terminal coverage multiplier &mdash; <em>not</em> a real rollout from a trained (or even zero-shot) policy against the live environment. The prompts and agent answers below are author-written. The reward arithmetic (<code>r_t</code>, <code>B</code>, <code>R</code>) is computed exactly as <code>env/reward.py</code> would compute it for the stated correctness pattern. A captured trace from a real model will replace this once the training client is released and run at scale (see <a href="#architecture" style="color:var(--accent2)">Architecture &amp; training pipeline</a>).
+    </div>
+
+    <div class="episode-trace">
+      <div class="trace-step">
+        <div class="step-marker good"></div>
+        <div class="step-label">Turn 1 &middot; dataset=PSML &middot; task=T1U:trend</div>
+        <div class="step-content">
+          <strong>Prompt:</strong> <em>&ldquo;Based on the array (length=336), report trend: upward / downward / constant.&rdquo;</em><br>
+          Series rises monotonically over the tail window.<br>
+          <strong>Agent answer:</strong> <code>"upward"</code> &nbsp;&rarr;&nbsp; <strong>correct</strong> (<code>r_1 = 1.0</code>)
+        </div>
+      </div>
+      <div class="trace-step">
+        <div class="step-marker good"></div>
+        <div class="step-label">Turn 3 &middot; dataset=freshretailnet &middot; task=T1U:seasonality</div>
+        <div class="step-content">
+          <strong>Prompt:</strong> daily demand for a fresh-retail SKU, peaks repeat every 7 steps with stable amplitude.<br>
+          <strong>Agent answer:</strong> <code>"fixed"</code> &nbsp;&rarr;&nbsp; <strong>correct</strong> (<code>r_3 = 1.0</code>)
+        </div>
+      </div>
+      <div class="trace-step">
+        <div class="step-marker good"></div>
+        <div class="step-label">Turn 5 &middot; dataset=MIMIC &middot; task=T1U:outliers</div>
+        <div class="step-content">
+          <strong>Prompt:</strong> ICU vital trace with a single spike above <code>q95 + 3&middot;MAD</code>.<br>
+          <strong>Agent answer:</strong> <code>"sudden_spike"</code> &nbsp;&rarr;&nbsp; <strong>correct</strong> (<code>r_5 = 1.0</code>)
+        </div>
+      </div>
+      <div class="trace-step">
+        <div class="step-marker good"></div>
+        <div class="step-label">Turn 7 &middot; dataset=causal_chambers &middot; task=T3:S2</div>
+        <div class="step-content">
+          <strong>Prompt:</strong> wind-chamber actuator trace, contextual question on lagged response to a step input (capability <code>C4</code>).<br>
+          <strong>Agent answer:</strong> <code>"delayed_response"</code> &nbsp;&rarr;&nbsp; <strong>correct</strong> (<code>r_7 = 1.0</code>)
+        </div>
+      </div>
+      <div class="trace-step">
+        <div class="step-marker good"></div>
+        <div class="step-label">Turn 9 &middot; dataset=PSML &middot; task=T2_MCQ (terminal)</div>
+        <div class="step-content">
+          <strong>Prompt:</strong> &ldquo;Median demand level change (forecast horizon vs history)?&rdquo;<br>
+          <strong>Agent answer:</strong> <code>"Higher"</code> &nbsp;&rarr;&nbsp; <strong>correct</strong> (<code>r_9 = 1.0</code>)<br>
+          <strong>Episode totals:</strong> <code>C = 7 / 9</code> correct, all four domains covered &rarr; <code>m = 1.0</code>.<br>
+          <strong>Terminal bonus:</strong> <code>B = 0.5 &middot; (7/9) &middot; 1.0 &asymp; 0.389</code>.<br>
+          <strong>Total return:</strong> <code>R &asymp; 7.389</code>.
+        </div>
+      </div>
+      <div class="trace-verdict good">
+        Four-of-four coverage &rarr; full <strong>m = 1.0</strong> multiplier. Contrast: the same 7 / 9 accuracy with a missed cross-domain question (say, zero correct in MIMIC) would give <code>B &asymp; 0.311</code> and <code>R &asymp; 7.311</code>. The coverage term is the whole reason a PSML-only policy loses to a generalist.
+      </div>
+    </div>
+  </section>
+
+  <!-- 11. EVALUATION PROTOCOL & PROJECTED TARGETS -->
+  <section id="results">
+    <h2>Evaluation protocol &amp; projected targets</h2>
+
+    <div class="notice">
+      <strong>No trained-policy numbers yet.</strong> Available compute was exhausted before GRPO could be run to convergence and validated at a scale we consider meaningful. The &ldquo;Trained HF policy&rdquo; column therefore stays blank, and every number in the &ldquo;Projected targets&rdquo; table below is an <em>analytical</em> projection from the environment&rsquo;s structure, not a measurement. GRPO runs against this Space will populate the empty column once the public training client is released and run at scale &mdash; see the canonical training-status notice under <a href="#architecture">Architecture &amp; training pipeline</a>. No fabricated telemetry is shown.
+    </div>
+
+    <blockquote><strong>Purple baselines (AgentBeats).</strong> For deployed purple policies on the same benchmark lineage, see <a href="https://agentbeats.dev/sharma-yash01/melady-ts-purple-agentscope" target="_blank" style="color:var(--accent2)">Melady TS Purple (AgentScope)</a> and <a href="https://agentbeats.dev/sharma-yash01/melady-ts-base-purple-agent" target="_blank" style="color:var(--accent2)">Melady TS Base Purple Agent</a>.</blockquote>
+
+    <h3>Baselines</h3>
+    <p>The environment supports the following baseline policies out of the box (random + majority) plus an eval harness for zero-shot and trained LLMs. All run against the live OpenEnv WebSocket so numbers are directly comparable with the trained policy.</p>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Baseline</th><th>Policy</th><th>What it isolates</th></tr></thead>
+        <tbody>
+          <tr><td><strong>Random MCQ</strong></td><td>Uniform sample over <code>observation.options</code></td><td>Lower bound; beats zero only if options are imbalanced</td></tr>
+          <tr><td><strong>Majority-class</strong></td><td>Always pick the per-<code>task_type</code> modal label from the bank</td><td>Isolates how much accuracy is available from priors alone</td></tr>
+          <tr><td><strong>Zero-shot API LLM</strong></td><td>GPT / Claude / Gemini via the eval harness</td><td>Strong &ldquo;off-the-shelf&rdquo; ceiling before any post-training</td></tr>
+          <tr><td><strong>Zero-shot local LLM</strong></td><td>Qwen2.5-7B-Instruct served via vLLM</td><td>Planned backbone for GRPO fine-tuning against this environment once training runs land</td></tr>
+          <tr><td><strong>Trained HF policy</strong></td><td>GRPO checkpoint from rollouts on this OpenEnv</td><td>Tests whether post-training on TS-Benchmark episodes beats zero-shot</td></tr>
+        </tbody>
+      </table>
+    </div>
+
+    <h3>Projected targets</h3>
+    <p>The numbers below are <strong>analytical projections</strong> from the environment&rsquo;s structure, not empirical results. They exist to anchor what &ldquo;good&rdquo; looks like once training runs land.</p>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Metric</th><th>Random MCQ</th><th>Zero-shot LLM (expected)</th><th>Target for trained policy</th></tr></thead>
+        <tbody>
+          <tr><td>Per-step accuracy (T1U &amp; T3, 3&ndash;4 options)</td><td class="num">&asymp; 0.25&ndash;0.33</td><td class="num">&gt;&gt; 0.33</td><td class="num">&ge; strong zero-shot</td></tr>
+          <tr><td>Per-step accuracy (T2_MCQ, 4 options)</td><td class="num">&asymp; 0.25</td><td class="num">&gt; random</td><td class="num">&ge; strong zero-shot</td></tr>
+          <tr><td>Coverage multiplier <code>m</code></td><td class="num">usually 0.8</td><td class="num">0.8&ndash;1.0</td><td class="num">1.0 consistently</td></tr>
+          <tr><td>Episode bonus <code>B</code> (&lambda;<sub>ep</sub>=0.5, N=9)</td><td class="num">&asymp; 0.10</td><td class="num">0.20&ndash;0.35</td><td class="num">&ge; 0.40</td></tr>
+        </tbody>
+      </table>
+    </div>
+    <h3>Current status</h3>
+    <p>What is already in place:</p>
+    <ul>
+      <li><strong>Full environment</strong>: <code>reset</code> / <code>step</code> / <code>state</code>, nine-question stratified sampling across four domains, per-step + terminal reward with coverage multiplier.</li>
+      <li><strong>Production banks</strong>: 2,775 <code>TSQuestion</code> records built from <code>TS-benchmark/task_merged_dev_with_labels_tiers.jsonl</code> via <code>TS-benchmark/scripts/build_temporal_bench_openenv_banks.py</code> and vendored under <code>openenv-ts/TemporalBenchEnv/data/banks/</code> (PSML 750 / freshretailnet 616 / MIMIC 709 / causal_chambers 700).</li>
+      <li><strong>Deployment path</strong>: <code>openenv.yaml</code>, Docker image (<code>server/Dockerfile</code>, <code>TEMPORALBENCH_QUESTION_BANK_DIR=/app/env/data/banks</code>), <code>openenv validate</code> passes, <code>openenv push</code> ready for HF Space.</li>
+      <li><strong>Training scaffolding</strong>: the intended path mirrors <a href="https://github.com/sharma-yash01/LotteryElicitationPT" target="_blank" style="color:var(--accent2)"><code>LotteryElicitationPT</code></a> (GRPO + TRL 1.0 <code>rollout_func</code> + vLLM). Training-client release status: see <a href="#architecture">Architecture</a>.</li>
+      <li><strong>Purple agents (AgentBeats)</strong>: live eval targets include <a href="https://agentbeats.dev/sharma-yash01/melady-ts-purple-agentscope" target="_blank" style="color:var(--accent2)">Melady TS Purple (AgentScope)</a> and <a href="https://agentbeats.dev/sharma-yash01/melady-ts-base-purple-agent" target="_blank" style="color:var(--accent2)">Melady TS Base Purple Agent</a>. Planned additions: see <a href="#purple-harnesses">&sect; Purple agent harnesses</a>.</li>
+    </ul>
+  </section>
+
+  <!-- 12. ENGINEERING LESSONS (INHERITED) -->
+  <section id="engineering">
+    <h2>Engineering lessons (inherited)</h2>
+    <p>Because TemporalBenchEnv intentionally rides on the same deployment + training pattern as <a href="https://huggingface.co/spaces/yashu2000/LotteryElicitationEnv" target="_blank" style="color:var(--accent2)">LotteryElicitationEnv</a> and <a href="https://huggingface.co/spaces/yashu2000/reasoning-economic-env" target="_blank" style="color:var(--accent2)">ReasoningEconomicsEnv</a>, most of the hard infra lessons are <em>inherited</em> rather than rediscovered. We document them briefly so the next submitter does not need to learn them from scratch.</p>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Issue</th><th>Root cause</th><th>Fix (inherited)</th></tr></thead>
+        <tbody>
+          <tr>
+            <td><strong>NCCL desync</strong> under variable-length episodes</td>
+            <td>In <code>vllm_mode=server</code>, different DDP ranks make different numbers of <code>generate()</code> calls per episode &rarr; sequence-numbered NCCL collectives go out of sync.</td>
+            <td>Fixed-count <code>generate()</code> padding per episode; dummy generates discarded via <code>_temporary_vllm_max_tokens(..., 1)</code>. Gated on <code>world_size &gt; 1</code>.</td>
+          </tr>
+          <tr>
+            <td><strong><code>max_completion_length</code></strong> drift over multi-turn rollouts</td>
+            <td>The rollout function appends per-turn generations + observation suffixes each step; a 9-turn MCQ episode can easily exceed the nominal completion budget.</td>
+            <td>Hard-cap <code>completion_ids</code> to <code>max_completion_length</code>; <code>PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True</code>.</td>
+          </tr>
+          <tr>
+            <td><strong>Null-safe MCQ parsing</strong></td>
+            <td>LLMs sometimes emit <code>{"answer": null}</code> or malformed strings; one rank crashing kills all DDP ranks via gloo cascade.</td>
+            <td>Inherited <code>_safe_float</code> / <code>_safe_int</code> / null-string fallbacks; fallback action rather than crash. The same regression pattern from <a href="https://github.com/sharma-yash01/LotteryElicitationPT" target="_blank" style="color:var(--accent2)">LotteryElicitationPT</a> applies directly to MCQ labels.</td>
+          </tr>
+          <tr>
+            <td><strong><code>openenv validate</code> hygiene</strong></td>
+            <td>OpenEnv&rsquo;s CLI does a naive substring check for <code>main()</code> in <code>server/app.py</code>; an entrypoint like <code>main(port=args.port)</code> fails validation.</td>
+            <td>Match the Lottery pattern: <code>if __name__ == "__main__": main()</code>, with flags parsed via <code>parse_known_args</code> inside <code>main()</code>.</td>
+          </tr>
+        </tbody>
+      </table>
+    </div>
+  </section>
+
+  <!-- 13. FOUNDATIONS & CITATIONS -->
+  <section id="foundations">
+    <h2>Foundations &amp; citations</h2>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Foundation</th><th>Role in this project</th><th>Citation</th></tr></thead>
+        <tbody>
+          <tr><td><strong>TS-Benchmark (ours)</strong></td><td>Task taxonomy (T1/T2/T3/T4), per-dataset label construction, MCQ question shape</td><td><a href="https://openreview.net/forum?id=rp5qCsxXca&noteId=jYZcZ4GI0y" target="_blank" style="color:var(--accent2)">OpenReview</a> &middot; in-repo: <code>TS-benchmark/TS-Benchmark.md</code></td></tr>
+          <tr><td><strong>Melady TS Green Agent</strong></td><td>A2A evaluator whose task set TemporalBenchEnv re-exposes as an OpenEnv environment</td><td><a href="https://agentbeats.dev/sharma-yash01/melady-ts-green-agent" target="_blank" style="color:var(--accent2)">AgentBeats</a> &middot; <a href="https://github.com/sharma-yash01/Melady_Agent-TS-Green" target="_blank" style="color:var(--accent2)">GitHub repository</a></td></tr>
+          <tr><td><strong>AgentScope</strong></td><td>Purple-agent harness &mdash; ReAct / MCP / A2A framework wrapping the backbone we post-train (<a href="#purple-harnesses">live on AgentBeats</a>)</td><td>Gao et al., <a href="https://arxiv.org/abs/2508.16279" target="_blank" style="color:var(--accent2)">arXiv:2508.16279</a> (2025) &middot; <a href="https://arxiv.org/abs/2402.14034" target="_blank" style="color:var(--accent2)">arXiv:2402.14034</a> (2024) &middot; <a href="https://github.com/agentscope-ai/agentscope" target="_blank" style="color:var(--accent2)">GitHub</a></td></tr>
+          <tr><td><strong>CAMEL</strong></td><td>Purple-agent harness &mdash; role-playing multi-agent society, CAMEL-backed baseline on AgentBeats</td><td>Li et al., <a href="https://arxiv.org/abs/2303.17760" target="_blank" style="color:var(--accent2)">NeurIPS 2023 (arXiv:2303.17760)</a> &middot; <a href="https://github.com/camel-ai/camel" target="_blank" style="color:var(--accent2)">GitHub</a></td></tr>
+          <tr><td><strong>MetaGPT</strong></td><td>Purple-agent harness (fast-follow-up) &mdash; SOP-driven multi-agent system, role-decomposition purple</td><td>Hong et al., <a href="https://arxiv.org/abs/2308.00352" target="_blank" style="color:var(--accent2)">ICLR 2024, oral (arXiv:2308.00352)</a> &middot; <a href="https://github.com/FoundationAgents/MetaGPT" target="_blank" style="color:var(--accent2)">GitHub</a></td></tr>
+          <tr><td><strong>TimeSeriesScientist (TSci)</strong></td><td>Purple-agent harness (fast-follow-up) &mdash; TS-specialized Curator/Planner/Forecaster/Reporter agent; &ldquo;ceiling&rdquo; for agentic TS reasoning</td><td>Zhao et al., <a href="https://arxiv.org/abs/2510.01538" target="_blank" style="color:var(--accent2)">arXiv:2510.01538</a> (2025) &middot; <a href="https://github.com/Y-Research-SBU/TimeSeriesScientist" target="_blank" style="color:var(--accent2)">GitHub</a></td></tr>
+          <tr><td><strong>FreshRetailNet-50K</strong></td><td>Retail demand dataset; T1/T2/T3/T4 MCQ questions</td><td>Ding et al., <a href="https://arxiv.org/abs/2505.16319" target="_blank" style="color:var(--accent2)">arXiv:2505.16319</a>, 2025</td></tr>
+          <tr><td><strong>PSML</strong></td><td>Power-system load dataset; primary domain for the default episode</td><td><a href="https://www.nature.com/articles/s41597-022-01455-7" target="_blank" style="color:var(--accent2)">Nature Sci. Data 2022</a></td></tr>
+          <tr><td><strong>MIMIC-IV</strong></td><td>ICU/EHR time-series dataset; medical-domain MCQ</td><td><a href="https://www.nature.com/articles/s41597-022-01899-x" target="_blank" style="color:var(--accent2)">Nature Sci. Data 2022</a></td></tr>
+          <tr><td><strong>Causal Chambers</strong></td><td>Physical-testbed TS dataset; contextual T3 and wind-chamber T1U</td><td><a href="https://www.nature.com/articles/s42256-024-00964-x" target="_blank" style="color:var(--accent2)">Nature MI 2024</a></td></tr>
+          <tr><td><strong>TimeMaster / COUNTS / SenTSR-Bench</strong></td><td>Prior art on composite RL rewards for TS-LLMs; motivates T2/T4 forecasting future work</td><td><a href="https://arxiv.org/abs/2506.13705" target="_blank" style="color:var(--accent2)">TimeMaster (arXiv:2506.13705)</a> &middot; <a href="https://arxiv.org/abs/2510.01116" target="_blank" style="color:var(--accent2)">COUNTS (arXiv:2510.01116)</a> &middot; <a href="https://arxiv.org/abs/2602.19455" target="_blank" style="color:var(--accent2)">SenTSR-Bench (arXiv:2602.19455)</a></td></tr>
+          <tr><td><strong>OpenEnv</strong></td><td>Gym-style reset/step, WebSocket transport, HF Space deployment</td><td><a href="https://huggingface.co/blog/openenv" target="_blank" style="color:var(--accent2)">HF Blog: Introducing OpenEnv</a></td></tr>
+          <tr><td><strong>TRL + GRPO</strong></td><td>GRPOTrainer, custom <code>rollout_func</code>, remote env rollouts</td><td>Shao et al., <a href="https://arxiv.org/abs/2402.03300" target="_blank" style="color:var(--accent2)">arXiv:2402.03300</a> (DeepSeekMath) &middot; <a href="https://huggingface.co/docs/trl/en/openenv" target="_blank" style="color:var(--accent2)">TRL &times; OpenEnv</a></td></tr>
+          <tr><td><strong>LotteryElicitationEnv / PT</strong></td><td>Sibling project &mdash; structural template for env / PT split, <code>rollout_func</code>, DDP padding, validation hygiene</td><td><a href="https://huggingface.co/spaces/yashu2000/LotteryElicitationEnv" target="_blank" style="color:var(--accent2)">HF Space (Env)</a> &middot; <a href="https://github.com/sharma-yash01/LotteryElicitationPT" target="_blank" style="color:var(--accent2)">GitHub (PT)</a></td></tr>
+        </tbody>
+      </table>
+    </div>
+  </section>
+
+  <!-- 14. QUICK START -->
+  <section id="quickstart">
+    <h2>Quick start</h2>
+    <pre><code><span class="c"># 1. Install and run the env locally</span>
+cd openenv-ts/TemporalBenchEnv
+uv sync --extra dev
+uv run pytest tests/ -q
+uv run openenv validate
+
+<span class="c"># 2. Run the server (uvicorn, port 8000)</span>
+uv run server
+<span class="c"># or</span>
+uvicorn server.app:app --reload
+
+<span class="c"># 3. Build &amp; run the Docker image</span>
+docker build -t temporalbenchenv:latest -f server/Dockerfile .
+docker run --rm -p 8000:8000 \
+    -e TEMPORALBENCH_QUESTION_BANK_DIR=/app/env/data/banks \
+    temporalbenchenv:latest
+
+<span class="c"># 4. Or pull / push a HF Space</span>
+export ENV_BASE_URL="https://huggingface.co/spaces/yashu2000/TemporalBenchEnv"
+openenv push   <span class="c"># from the TemporalBenchEnv/ directory</span>
+
+<span class="c"># 5. Minimal client usage</span>
+python - &lt;&lt;'PY'
+from client import TemporalBenchAction, TemporalBenchEnvClient
+with TemporalBenchEnvClient(base_url=&quot;http://localhost:8000&quot;) as env:
+    out = env.reset()
+    while not out.done:
+        q = out.observation
+        out = env.step(TemporalBenchAction(answer=q.options[0]))
+    print(&quot;total reward:&quot;, out.observation.reward)
+PY
+
+<span class="c"># 6. GRPO / TRL training against this Space (training client under test; not on GitHub yet)</span>
+<span class="c"># Release is planned after additional compute for reasonable model validation.</span></code></pre>
+    <p><strong>Purple track (AgentBeats):</strong> <a href="https://agentbeats.dev/sharma-yash01/melady-ts-purple-agentscope" target="_blank" style="color:var(--accent2)">Melady TS Purple (AgentScope)</a> &middot; <a href="https://agentbeats.dev/sharma-yash01/melady-ts-base-purple-agent" target="_blank" style="color:var(--accent2)">Melady TS Base Purple Agent</a>.</p>
+    <p>Banks are reproducible from <code>(env_seed, curriculum_stage, primary_domain)</code>. No external fixtures, no live API, no human labels &mdash; the same flat tiered JSONL that drives the Melady TS Green Agent drives this environment.</p>
+  </section>
+
+  <div class="callout">
+    <div class="q">Can an LLM post-trained on our Green Agent&rsquo;s own benchmark outperform zero-shot baselines at cross-domain TS reasoning?</div>
+    <div class="sub">The environment is built and deployed. Training-client release and empirical GRPO numbers are contingent on compute availability &mdash; see <a href="#architecture" style="color:var(--accent2)">Architecture</a>.</div>
+    <div class="sub" style="margin-top:.85rem;">On AgentBeats, compare purple baselines such as <a href="https://agentbeats.dev/sharma-yash01/melady-ts-purple-agentscope" target="_blank" style="color:var(--accent2)">Melady TS Purple (AgentScope)</a> and <a href="https://agentbeats.dev/sharma-yash01/melady-ts-base-purple-agent" target="_blank" style="color:var(--accent2)">Melady TS Base Purple Agent</a>.</div>
+  </div>
+
+  <!-- 15. FUTURE WORK -->
+  <section id="future">
+    <h2>Future work</h2>
+    <ul>
+      <li><strong>Run GRPO to convergence</strong> on Stage 1 &rarr; Stage 2 &rarr; Stage 3 curriculum against this environment and fill in the &ldquo;Trained HF policy&rdquo; column of the baselines table (blocked on releasing the training client; see Architecture). Relate outcomes to AgentBeats purple baselines (<a href="https://agentbeats.dev/sharma-yash01/melady-ts-purple-agentscope" target="_blank" style="color:var(--accent2)">AgentScope</a> / <a href="https://agentbeats.dev/sharma-yash01/melady-ts-base-purple-agent" target="_blank" style="color:var(--accent2)">CAMEL</a>) and, once landed, the planned MetaGPT / TSci harnesses.</li>
+      <li><strong>T2 / T4 forecasting reward</strong> &mdash; replace the <code>compute_forecasting_reward</code> stub with the composite sketched in our v1 plan (normalized MSE + DTW-shape + direction + quantile + ArcTan-smoothed MAE). Opens T2 numeric and T4 contextual forecasting tasks without touching the step loop.</li>
+      <li><strong>Confidence-calibration reward</strong> &mdash; <code>TemporalBenchAction.confidence</code> already exists; shape rewards around Brier-style calibration so the policy is incentivized to <em>know when it knows</em>.</li>
+      <li><strong>CoT / reasoning reward</strong> &mdash; <code>TemporalBenchAction.reasoning</code> is captured per step but currently unscored; a light format-plus-consistency shaping mirrors the Lottery format-weight technique.</li>
+      <li><strong>Token-budget curriculum</strong> (ReasoningEconomics-style) &mdash; constrain per-episode reasoning length to force terseness at higher stages.</li>
+      <li><strong>Soft 60 / 40 task-type mix</strong> &mdash; enforce the v1-plan ratio of understanding (T1U+T3) to prediction (T2_MCQ) explicitly in <code>EpisodeSampler</code>; today it emerges from bank sizes.</li>
+      <li><strong>SFT warm-up</strong> on valid MCQ JSON before GRPO &mdash; skips the cold-start formatting phase we hit in Lottery.</li>
+      <li><strong>Human-subjects transfer</strong> &mdash; once the policy beats zero-shot on our bank, measure sim-to-real with held-out TS data outside the Green Agent&rsquo;s training split.</li>
+    </ul>
+  </section>
+
+  <!-- 16. CONCLUSION -->
+  <section>
+    <h2>Conclusion</h2>
+    <p><strong>TemporalBenchEnv</strong> is the training-side companion to our Melady TS Green Agent&rsquo;s eval-side role. The Green Agent ranks purple agents on TS-Benchmark through the A2A protocol; TemporalBenchEnv re-exposes the same four-dataset MCQ suite as an OpenEnv-native sequential MDP, so an LLM can be <em>post-trained</em> on the very benchmark it will later be scored against.</p>
+    <p>Every design choice &mdash; 9-question episodes with 6 primary + 3 cross-domain, per-step correctness + terminal bonus with a domain-coverage multiplier, stagewise curriculum T1U &rarr; +T3 &rarr; +T2_MCQ, and a strict separation of env server and trainer over WebSocket &mdash; is aimed at preserving the verifiability that made the Green Agent&rsquo;s signal trustworthy, while turning it into gradient. The infrastructure contributions are mostly <em>inherited</em> from our Lottery / Reasoning siblings; the novelty is the domain binding and the Green-Agent &rarr; OpenEnv pipeline.</p>
+    <p>The research question is open: <em>can a GRPO-trained LLM, post-trained on our own benchmark, outperform strong zero-shot baselines at cross-domain TS reasoning?</em> The environment, banks, and deployment path are shipped; full training results await the public training client and the compute budget described under Architecture. The purple track stays live on AgentBeats via <a href="https://agentbeats.dev/sharma-yash01/melady-ts-purple-agentscope" target="_blank" style="color:var(--accent2)">Melady TS Purple (AgentScope)</a> and <a href="https://agentbeats.dev/sharma-yash01/melady-ts-base-purple-agent" target="_blank" style="color:var(--accent2)">Melady TS Base Purple Agent</a>.</p>
+  </section>
+
+  <div class="footer">
+    <p>TemporalBenchEnv &middot; Melady / AgentX OpenEnv Track &middot; USC</p>
+    <p style="margin-top:.5rem;">
+      <a href="https://github.com/sharma-yash01/TemporalBenchEnv" target="_blank">GitHub</a> &middot;
+      <a href="https://huggingface.co/spaces/yashu2000/TemporalBenchEnv" target="_blank">HF Space</a> &middot;
+      <a href="https://agentbeats.dev/sharma-yash01/melady-ts-green-agent" target="_blank">Melady TS Green Agent</a> &middot;
+      <a href="https://github.com/sharma-yash01/Melady_Agent-TS-Green" target="_blank">Green Agent GitHub</a> &middot;
+      <a href="https://github.com/meta-pytorch/OpenEnv" target="_blank">OpenEnv Framework</a> &middot;
+      <a href="https://huggingface.co/docs/trl/en/openenv" target="_blank">TRL &times; OpenEnv</a>
+    </p>
+  </div>
+
+</div>
+</body>
 </html>